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Emerging topics in modeling interval-censored survival data / / Jianguo Sun, Ding-Geng Chen, editors
| Emerging topics in modeling interval-censored survival data / / Jianguo Sun, Ding-Geng Chen, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (322 pages) |
| Disciplina | 519.52 |
| Collana | ICSA book series in statistics |
| Soggetto topico |
Censored observations (Statistics)
Statistics - Methodology Estadística Metodologia |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-12366-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Part I: Introduction and Review (Chapters 1 -3) -- Part II: Emerging Topics in Methodology (Chapters 4 -9) -- Part III: Emerging Topics in Applications (Chapters 10 -15) -- Contents -- Editors and Contributors -- About the Editors -- Contributors -- Part I Introduction and Review -- Overview of Historical Developments in Modeling Interval-Censored Survival Data -- 1 Emerging Interval-Censored Data -- 2 Emerging Methods in Analyzing Interval-Censored Data -- 3 More on Emerging Methods in Analyzing Interval-Censored Data -- References -- Overview of Recent Advances on the Analysis of Interval-Censored Failure Time Data -- 1 Introduction -- 2 Regression Analysis of Univariate Interval-Censored Failure Time Data -- 2.1 Regression Analysis with Time-Dependent Covariates -- 2.2 Regression Analysis in the Presence of a Cured Subgroup -- 2.3 Variable Section for Interval-Censored Data -- 3 Regression Analysis with Informative Interval Censoring -- 4 Regression Analysis of Clustered and Multivariate Interval-Censored Data -- 5 Other Topics on Regression Analysis of Interval-Censored Data -- 6 Concluding Remarks -- References -- Predictive Accuracy of Prediction Model for Interval-Censored Data -- 1 Introduction -- 2 Time-dependent AUC -- 2.1 Review of ROC Curve -- 2.2 ROC for Interval Censored Data -- 2.3 Simulation -- 3 Time-Dependent C-index -- 3.1 Review of C-index -- 3.2 C-index for Interval Censored Data -- 3.3 Simulation -- 4 Brier Score -- 4.1 Review of Brier Score -- 4.2 Brier Score for Interval Censored Data -- 4.3 Simulation -- 5 Application to Dementia Dataset -- 6 Concluding Remarks -- Appendix: R code -- References -- Part II Emerging Topics in Methodology -- A Practical Guide to Exact Confidence Intervals for a Distribution of Current Status Data Using the Binomial Approach -- 1 Introduction.
2 Current Status Data and Point Estimations -- 2.1 Current Status Data -- 2.2 The R package csci: Current Status Confidence Intervals -- 2.3 Point Estimation for F(t) -- 3 Valid Binomial Approach Confidence Intervals -- 3.1 A Structure of the Valid Confidence Interval for F(t) -- 3.2 A Specific Form of the Functions a(t,n,C) and b(t,n,C) -- 3.3 Choice of mn -- 4 The ABA (Approximate Binomial Approach) Confidence Intervals -- 4.1 The Structure of the ABA Confidence Interval -- 4.2 Choice of m†n -- 4.3 Aesthetic Adjustments -- 5 Simulation Studies -- 5.1 Simulation 1 -- 5.2 Simulation 2 -- 6 Analyzing the Hepatitis A Data in Bulgaria -- 7 Conclusion -- References -- Accelerated Hazards Model and Its Extensions for Interval-Censored Data -- 1 Why Is Accelerated Hazards Model Needed? -- 2 Accelerated Hazards Model with Interval-Censored Data -- 3 Estimation Procedure -- 3.1 Sieve Semiparametric Maximum Likelihood Estimator -- 3.2 Implementation -- 3.3 Choosing the Number of Base Splines -- 4 Large Sample Properties -- 5 Simulation Study -- 6 Example 1: Diabetes Conversion Data -- 7 Extensions of Accelerated Hazards Model -- 7.1 Generalized Accelerated Hazards Model -- 7.2 GAH Mixture Cure Model -- 8 Sieve Maximum Likelihood Estimation for GAHCure Model -- 8.1 Sieve Likelihood -- 8.2 Algorithm -- 8.3 Simulation Results -- 9 Example 2: Smoking Cessation Data -- References -- Maximum Likelihood Estimation of Semiparametric Regression Models with Interval-Censored Data -- 1 Introduction -- 2 Cox Model and Right-Censored Data -- 3 Interval-Censored Data -- 4 Competing Risks -- 5 Multivariate Failure Time Data -- 6 Remarks -- References -- Use of the INLA Approach for the Analysis of Interval-Censored Data -- 1 Introduction -- 2 Approximate Bayesian Inference with INLA -- 2.1 INLA -- 2.2 The R-INLA Package -- 3 Interval Censored Survival Analysis with INLA. 3.1 Survival Models as LGMs -- 3.2 Capabilities and Possibilities for Survival Models in INLA -- 4 Examples -- 4.1 Diabetic Nephropathy: Frailty Log-Logistic Model -- 4.1.1 Frailty Log-Logistic Model as a Latent Gaussian Model -- 4.1.2 Using R-INLA for Full Bayesian Inference -- 4.1.3 Results -- 4.2 Epilepsy Drug Efficacy: Non-linear Joint Model with Competing Risks and Interval Censoring -- 4.2.1 Non-linear Joint Model with Competing Risks as a Latent Gaussian Model -- 4.2.2 Results -- 5 Discussion -- Appendix -- References -- Copula Models and Diagnostics for Multivariate Interval-Censored Data -- 1 Introduction -- 2 Notation and Methods -- 2.1 Copula Model for Multivariate Interval-Censored Data -- 2.2 Joint Likelihood for Bivariate Interval-Censored Data -- 2.3 Choice of Marginal Models -- 3 Parameter Estimation -- 3.1 Sieve Likelihood with Bernstein Polynomials -- 4 Goodness-of-Fit Test for Copula Specification -- 4.1 Hypothesis and Test Statistic -- 4.2 Estimation of IR Statistic -- 4.3 Test Procedure -- 5 Simulation Studies -- 5.1 Generating Bivariate Interval-Censored Times -- 5.2 Simulation-I: Parameter Estimation -- 5.3 Simulation-II: Joint Survival Probability Estimation Performance -- 5.4 Simulation III: IR Test Performance -- 6 Real Examples -- 7 Conclusion -- References -- Efficient Estimation of the Additive Risks Model for Interval-Censored Data -- 1 Introduction -- 2 Statistical Model -- 2.1 Notations and Setup -- 2.2 Likelihood -- 3 Estimation -- 3.1 MM Algorithm -- 3.2 Variance Estimation -- 3.3 Complexity Analysis -- 4 Simulation Study -- 5 Application: Breast Cancer Data -- 6 Implementation: R Package MMIntAdd -- 7 Conclusions -- Appendix -- Proof of Theorem 1 -- References -- Part III Emerging Topics in Applications -- Modeling and Analysis of Chronic Disease Processes Under Intermittent Observation -- 1 Introduction. 2 Modeling Multistate Disease and Observation Processes -- 2.1 Multistate Models -- 2.2 Joint Models for the Disease and Visit Process -- 3 Partially Specified Models for Marginal Features -- 4 Cox Models with Markers Under Intermittent Observation -- 4.1 Joint Marker-Failure Visit Process Models -- 4.2 Limiting Value of a Cox Model Coefficient Under LOCF -- 4.3 A Bone Marker and Event-Free Survival -- 5 Discussion -- References -- Case-Cohort Studies with Time-Dependent Covariates and Interval-Censored Outcome -- 1 Introduction -- 2 Model Specification -- 2.1 Full Cohort -- 2.2 Case-Cohort -- 3 Simulation -- 4 Hormonal Contraceptive HIV Data -- 5 Discussion -- Supporting Information -- References -- The BivarIntCensored: An R Package for Nonparametric Inference of Bivariate Interval-Censored Data -- 1 Introduction -- 2 Method -- 2.1 Spline-Based Sieve NPMLE -- 2.1.1 Notation -- 2.1.2 Likelihood Function -- 2.1.3 Spline-Based Sieve NPMLE -- 2.2 A Nonparametric Association Test -- 3 Implementation -- 4 BivarIntCensored Package and Its Illustration -- 4.1 Main Functions -- 4.2 Example -- 5 Conclusions -- References -- Joint Modeling for Longitudinal and Interval-Censored Survival Data: Application to IMPI Multi-Center HIV/AIDS Clinical Trial -- 1 Introduction -- 2 Data and Methods -- 2.1 Data Structure -- 2.1.1 Survival Data -- 2.1.2 Longitudinal Data -- 2.2 The Joint Model -- 2.2.1 The Shared Parameter Joint Model -- 2.2.2 The Joint Models for Interval-Censored Data -- 3 Data Analysis -- 3.1 Illustration Using the IMPI Trial Data -- 3.2 Survival Data Analysis with Time-Dependent Covariates -- 3.3 Longitudinal Data Analysis: Linear Mixed-Effects Model -- 3.4 Joint Modeling for Longitudinal CD4 Counts and Interval-Censored Survival Data -- 4 Discussions -- References. Regression Analysis with Interval-Censored Covariates. Application to Liquid Chromatography -- 1 Introduction -- 1.1 Interval-Censored Covariates in Regression Models: State of the Art -- 1.2 Outline -- 2 Motivating Data -- 2.1 Notation -- 2.1.1 Single Compounds -- 2.1.2 Sum of Compounds -- 3 Regression Methods Accounting for Limits of Detection and Quantitation -- 3.1 The GEL Method -- 3.2 Extension to the Generalized Linear Model -- 3.2.1 Regression with the Gamma Distribution -- 3.2.2 Logistic Regression -- 3.3 Comments on the Inclusion of Exact Observations -- 3.4 Residuals for Interval-Censored Covariates -- 3.4.1 Residuals for the Linear Model -- 3.4.2 Extension of GEL Residuals to the Generalized Linear Model -- 3.5 Implementation -- 4 Illustration -- 4.1 Linear Regression Model to Model log(Glucose) -- 4.2 Gamma Regression Model to Model Glucose Levels -- 4.3 Logistic Regression Model for Association with Obesity -- 5 Discussion -- References -- Misclassification Simulation Extrapolation Procedure for Interval-Censored Log-Logistic Accelerated Failure Time Model -- 1 Introduction -- 2 Methodology -- 2.1 Interval-Censored (Type II) Survival Data and Log-Logistic AFT Model with Misclassification Matrix -- 2.2 MC-SIMEX -- 3 Monte-Carlo Simulation Study -- 3.1 Simulation Design -- 3.2 Results of Simulation -- 4 Real Data Analysis -- 5 Discussions -- References -- Index. |
| Record Nr. | UNISA-996499868103316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Non-parametric tests for censored data / / Vilijandas Bagdonavičius, Julius Kruopis, Mikhail S. Nikulin
| Non-parametric tests for censored data / / Vilijandas Bagdonavičius, Julius Kruopis, Mikhail S. Nikulin |
| Autore | Bagdonavičius V (Vilijandas) |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Wiley, 2013 |
| Descrizione fisica | 1 online resource (253 p.) |
| Disciplina | 519.5 |
| Altri autori (Persone) |
KruopisJulius
NikulinM. S (Mikhail Stepanovich) |
| Collana | ISTE |
| Soggetto topico |
Nonparametric statistics
Statistical hypothesis testing Censored observations (Statistics) |
| ISBN |
9781118558072
1118558073 9781118602133 1118602137 9781118601983 111860198X 9781299187658 129918765X |
| Classificazione | MAT003000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Non-parametric Tests for Censored Data; Title Page; Copyright Page; Table of Contents; Preface; Terms and Notation; Chapter 1. Censored and Truncated Data; 1.1. Right-censored data; 1.2. Left truncation; 1.3. Left truncation and right censoring; 1.4. Nelson-Aalen and Kaplan-Meier estimators; 1.5. Bibliographic notes; Chapter 2. Chi-squared Tests; 2.1. Chi-squared test for composite hypothesis; 2.2. Chi-squared test for exponential distributions; 2.3. Chi-squared tests for shape-scale distribution families; 2.3.1. Chi-squared test for the Weibull distribution
2.3.2. Chi-squared tests for the loglogistic distribution2.3.3. Chi-squared test for the lognormal distribution; 2.4. Chi-squared tests for other families; 2.4.1. Chi-squared test for the Gompertz distribution; 2.4.2. Chi-squared test for distribution with hyperbolic hazard function; 2.4.3. Bibliographic notes; 2.5. Exercises; 2.6. Answers; Chapter 3. Homogeneity Tests for Independent Populations; 3.1. Data; 3.2. Weighted logrank statistics; 3.3. Logrank test statistics as weighted sums of differences between observed and expected number of failures; 3.4. Examples of weights 3.5. Weighted logrank statistics as modified score statistics3.6. The first two moments of weighted logrank statistics; 3.7. Asymptotic properties of weighted logrank statistics; 3.8. Weighted logrank tests; 3.9. Homogeneity testing when alternatives are crossings of survival functions; 3.9.1. Alternatives; 3.9.2. Modified score statistics; 3.9.3. Limit distribution of the modified score statistics; 3.9.4. Homogeneity tests against crossing survival functions alternatives; 3.9.5. Bibliographic notes; 3.10. Exercises; 3.11. Answers; Chapter 4. Homogeneity Tests for Related Populations 4.1. Paired samples4.1.1. Data; 4.1.2. Test statistics; 4.1.3. Asymptotic distribution of the test statistic; 4.1.4. The test; 4.2. Logrank-type tests for homogeneity of related k > 2 samples; 4.3. Homogeneity tests for related samples against crossing marginal survival functions alternatives; 4.3.1. Bibliographic notes; 4.4. Exercises; 4.5. Answers; Chapter 5. Goodness-of-fit for Regression Models; 5.1. Goodness-of-fit for the semi-parametric Cox model; 5.1.1. The Cox model; 5.1.2. Alternatives to the Cox model based on expanded models; 5.1.3. The data and the modified score statistics 5.1.4. Asymptotic distribution of the modified score statistic5.1.5. Tests; 5.2. Chi-squared goodness-of-fit tests for parametric AFT models; 5.2.1. Accelerated failure time model; 5.2.2. Parametric AFT model; 5.2.3. Data; 5.2.4. Idea of test construction; 5.2.5. Asymptotic distribution of Hn and Z; 5.2.6. Test statistics; 5.3. Chi-squared test for the exponential AFT model.; 5.4. Chi-squared tests for scale-shape AFT models.; 5.4.1. Chi-squared test for the Weibull AFT model; 5.4.2. Chi-squared test for the lognormal AFT model; 5.4.3. Chi-squared test for the loglogistic AFT model 5.5. Bibliographic notes |
| Record Nr. | UNINA-9910808680503321 |
Bagdonavičius V (Vilijandas)
|
||
| Wiley, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Non-parametric tests for censored data [[electronic resource] /] / Vilijandas Bagdonavičius, Julius Kruopis, Mikhail S. Nikulin
| Non-parametric tests for censored data [[electronic resource] /] / Vilijandas Bagdonavičius, Julius Kruopis, Mikhail S. Nikulin |
| Autore | Bagdonavičius V (Vilijandas) |
| Pubbl/distr/stampa | London, : ISTE |
| Descrizione fisica | 1 online resource (253 p.) |
| Disciplina | 519.5 |
| Altri autori (Persone) |
KruopisJulius
NikulinM. S (Mikhail Stepanovich) |
| Collana | ISTE |
| Soggetto topico |
Nonparametric statistics
Statistical hypothesis testing Censored observations (Statistics) |
| ISBN |
1-118-55807-3
1-118-60213-7 1-118-60198-X 1-299-18765-X |
| Classificazione | MAT003000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Non-parametric Tests for Censored Data; Title Page; Copyright Page; Table of Contents; Preface; Terms and Notation; Chapter 1. Censored and Truncated Data; 1.1. Right-censored data; 1.2. Left truncation; 1.3. Left truncation and right censoring; 1.4. Nelson-Aalen and Kaplan-Meier estimators; 1.5. Bibliographic notes; Chapter 2. Chi-squared Tests; 2.1. Chi-squared test for composite hypothesis; 2.2. Chi-squared test for exponential distributions; 2.3. Chi-squared tests for shape-scale distribution families; 2.3.1. Chi-squared test for the Weibull distribution
2.3.2. Chi-squared tests for the loglogistic distribution2.3.3. Chi-squared test for the lognormal distribution; 2.4. Chi-squared tests for other families; 2.4.1. Chi-squared test for the Gompertz distribution; 2.4.2. Chi-squared test for distribution with hyperbolic hazard function; 2.4.3. Bibliographic notes; 2.5. Exercises; 2.6. Answers; Chapter 3. Homogeneity Tests for Independent Populations; 3.1. Data; 3.2. Weighted logrank statistics; 3.3. Logrank test statistics as weighted sums of differences between observed and expected number of failures; 3.4. Examples of weights 3.5. Weighted logrank statistics as modified score statistics3.6. The first two moments of weighted logrank statistics; 3.7. Asymptotic properties of weighted logrank statistics; 3.8. Weighted logrank tests; 3.9. Homogeneity testing when alternatives are crossings of survival functions; 3.9.1. Alternatives; 3.9.2. Modified score statistics; 3.9.3. Limit distribution of the modified score statistics; 3.9.4. Homogeneity tests against crossing survival functions alternatives; 3.9.5. Bibliographic notes; 3.10. Exercises; 3.11. Answers; Chapter 4. Homogeneity Tests for Related Populations 4.1. Paired samples4.1.1. Data; 4.1.2. Test statistics; 4.1.3. Asymptotic distribution of the test statistic; 4.1.4. The test; 4.2. Logrank-type tests for homogeneity of related k > 2 samples; 4.3. Homogeneity tests for related samples against crossing marginal survival functions alternatives; 4.3.1. Bibliographic notes; 4.4. Exercises; 4.5. Answers; Chapter 5. Goodness-of-fit for Regression Models; 5.1. Goodness-of-fit for the semi-parametric Cox model; 5.1.1. The Cox model; 5.1.2. Alternatives to the Cox model based on expanded models; 5.1.3. The data and the modified score statistics 5.1.4. Asymptotic distribution of the modified score statistic5.1.5. Tests; 5.2. Chi-squared goodness-of-fit tests for parametric AFT models; 5.2.1. Accelerated failure time model; 5.2.2. Parametric AFT model; 5.2.3. Data; 5.2.4. Idea of test construction; 5.2.5. Asymptotic distribution of Hn and Z; 5.2.6. Test statistics; 5.3. Chi-squared test for the exponential AFT model.; 5.4. Chi-squared tests for scale-shape AFT models.; 5.4.1. Chi-squared test for the Weibull AFT model; 5.4.2. Chi-squared test for the lognormal AFT model; 5.4.3. Chi-squared test for the loglogistic AFT model 5.5. Bibliographic notes |
| Record Nr. | UNINA-9910138865303321 |
|
Bagdonavičius V (Vilijandas)
|
||
| London, : ISTE | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Visual and MSR grades of lumber are not two-parameter Weibulls and why it matters (with a discussion of censored data fitting) / / Steve, P. Verrill [and four others]
| Visual and MSR grades of lumber are not two-parameter Weibulls and why it matters (with a discussion of censored data fitting) / / Steve, P. Verrill [and four others] |
| Autore | Verrill S. P. |
| Pubbl/distr/stampa | Madison, WI : , : United States Department of Agriculture, Forest Service, Forest Products Laboratory, , 2019 |
| Descrizione fisica | 1 online resource (40 pages) |
| Collana | Research paper FPL-RP |
| Soggetto topico |
Elasticity
Flexure - Mathematical models Strength of materials - Mathematical models Censored observations (Statistics) Wood - Mechanical properties - Mathematical models Wood - Elastic properties - Mathematical models Weibull distribution |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Visual and MSR grades of lumber are not two-parameter Weibulls and why it matters |
| Record Nr. | UNINA-9910712812903321 |
|
Verrill S. P.
|
||
| Madison, WI : , : United States Department of Agriculture, Forest Service, Forest Products Laboratory, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
